SOTAVerified

Image Classification

Image Classification is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike object detection, which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as image retrieval, which also involves finding similar images in a large database.

Source: Metamorphic Testing for Object Detection Systems

Papers

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Showing 2401–2450 of 10419 papers

TitleStatusHype
Learning Sparse & Ternary Neural Networks with Entropy-Constrained Trained Ternarization (EC2T)Code0
A Quantization-Friendly Separable Convolution for MobileNetsCode0
Cold Case: The Lost MNIST DigitsCode0
Investigation of Federated Learning Algorithms for Retinal Optical Coherence Tomography Image Classification with Statistical HeterogeneityCode0
Coherence Awareness in Diffractive Neural NetworksCode0
Investigating Weight-Perturbed Deep Neural Networks With Application in Iris Presentation Attack DetectionCode0
Spurious Feature Eraser: Stabilizing Test-Time Adaptation for Vision-Language Foundation ModelCode0
Invariant Shape Representation Learning For Image ClassificationCode0
Investigating the Corruption Robustness of Image Classifiers with Random Lp-norm CorruptionsCode0
A Continual Development Methodology for Large-scale Multitask Dynamic ML SystemsCode0
A Programmable Approach to Neural Network CompressionCode0
Invariant backpropagation: how to train a transformation-invariant neural networkCode0
Coarse-to-Fine Object Tracking Using Deep Features and Correlation FiltersCode0
Invariance encoding in sliced-Wasserstein space for image classification with limited training dataCode0
Intra-class Patch Swap for Self-DistillationCode0
A Group-Theoretic Framework for Data AugmentationCode0
IPCL: Iterative Pseudo-Supervised Contrastive Learning to Improve Self-Supervised Feature RepresentationCode0
CoA: Chain-of-Action for Generative Semantic LabelsCode0
Adversarial-Playground: A Visualization Suite Showing How Adversarial Examples Fool Deep LearningCode0
Interpretable Network Visualizations: A Human-in-the-Loop Approach for Post-hoc Explainability of CNN-based Image ClassificationCode0
CNNtention: Can CNNs do better with Attention?Code0
Interpretable and Interactive Deep Multiple Instance Learning for Dental Caries Classification in Bitewing X-raysCode0
Rethinking Pre-Trained Feature Extractor Selection in Multiple Instance Learning for Whole Slide Image ClassificationCode0
InterpNET: Neural Introspection for Interpretable Deep LearningCode0
CNN-RNN: A Unified Framework for Multi-label Image ClassificationCode0
CNN Features off-the-shelf: an Astounding Baseline for RecognitionCode0
Interferometric Neural NetworksCode0
Interlocking Backpropagation: Improving depthwise model-parallelismCode0
Intelligent Multi-View Test Time AugmentationCode0
A Computing Kernel for Network Binarization on PyTorchCode0
An Intelligent Remote Sensing Image Quality Inspection SystemCode0
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)Code0
Interpret Your Decision: Logical Reasoning Regularization for Generalization in Visual ClassificationCode0
Instance-dependent Label Distribution Estimation for Learning with Label NoiseCode0
C-Net: A Reliable Convolutional Neural Network for Biomedical Image ClassificationCode0
Instance Enhancement Batch Normalization: an Adaptive Regulator of Batch NoiseCode0
Instance Temperature Knowledge DistillationCode0
CMVAE: Causal Meta VAE for Unsupervised Meta-LearningCode0
Approximate Manifold Defense Against Multiple Adversarial PerturbationsCode0
Instance-based Label Smoothing For Better Calibrated Classification NetworksCode0
Instilling Inductive Biases with SubnetworksCode0
In-Place Activated BatchNorm for Memory-Optimized Training of DNNsCode0
Initialization Matters for Adversarial Transfer LearningCode0
Input-gradient space particle inference for neural network ensemblesCode0
ClusterFit: Improving Generalization of Visual RepresentationsCode0
Approximate Fisher Information Matrix to Characterise the Training of Deep Neural NetworksCode0
Input Invex Neural NetworkCode0
Clustered Task-Aware Meta-Learning by Learning from Learning PathsCode0
Information Competing Process for Learning Diversified RepresentationsCode0
Adversarial Learning for Personalized Tag RecommendationCode0
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All datasetsImageNetCIFAR-10CIFAR-100STL-10ObjectNetMNISTSVHNiNaturalist 2018ImageNet ReaLFlowers-102Clothing1Mmini WebVision 1.0

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91—Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98—Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94—Unverified
4DaViT-GTop 1 Accuracy90.4—Unverified
5DaViT-HTop 1 Accuracy90.2—Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2—Unverified
7SwinV2-GTop 1 Accuracy90.17—Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1—Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05—Unverified
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified